Rafael Meza Department of Epidemiology University of Michigan Experiences Sharing Data and Models from a Multi-Institutional Cancer Modeling Consortium Rafael Meza Department of Epidemiology University of Michigan 1

CISNET Cancer Intervention and Surveillance Modeling Network NCI-sponsored collaborative consortium of simulation modelers in breast, prostate, colorectal, lung, and esophageal cancers formed in 2000 Funded through a U01 (collaborative agreement) mechanism Use surveillance, epidemiology, clinical data and simulation modeling to guide public health research and priorities http://cisnet.cancer.gov

CISNET Lung Group Six lung cancer models: Smoking and lung cancer Erasmus, Georgetown, MGH, Michigan, Stanford and Yale Smoking and lung cancer Reconstruction of smoking histories in the US Impact of tobacco control on lung cancer outcomes and overall mortality Lung cancer screening Extrapolation of NLST/PLCO trial results to the US Assessment of alternative lung cancer screening strategies

Why so many models? The more the merrier? Comparative modeling approach “Independent” models but with comparable inputs and outputs Heterogeneity in model assumptions / data sources Address model misspecification When consensus can be reached, it greatly enhances the credibility of modeling results by highlighting their reproducibility

Why so many models? Comparative modeling approach – cont. When results are disparate, it can help to pinpoint areas where our knowledge base is insufficient and further research is needed Comparative modeling is a powerful tool that would be impossible without a collaborative effort within the scientific community Cited by the International Society Pharmacoeconomics and Outcomes Research Modeling TF as example of good modeling practices

Fred Hutchinson Lung Cancer Model – Model F Meza, ten Haaf et al, Cancer in press

Erasmus-MISCAN Model – Model E

MGH-HMS Lung Cancer Policy Model – Model M Meza, ten Haaf et al, Cancer in press

Stanford Natural History Model of Lung Cancer – Model S Meza, ten Haaf et al, Cancer in press

apm bpm m0 m1 apc bpc mpm lIA1 lIA2 lIB lII lIIIA lIIIB IA1 IA2 IB II Michigan Lung Cancer screening model – Model U By gender and histology (SC,AC,SQ,ONSCLC) Normal X Preinitiated Pre malignant apm bpm m0 m1 Preclinical apc bpc mpm Preclinical lIA1 lIA2 lIB lII lIIIA lIIIB IA1 IA2 IB II IIIA IIIB IV dIA1 dIB dII dIIIA dIIIB dIV dIA2 Clinical Detection Meza, ten Haaf et al, Cancer in press

Computational / Data / Documentation Issues 6 independent models Computational implementation Shared know-how Sharing inputs Large datasets, eg, large-scale screening clinical trials Input simulators, eg, smoking history generator Producing same outputs – to be compared Processing outputs from multiple models Result dissemination Common documentation – model profiler Model sharing / model real-time “running”

Collaboration tools

Shared Input Generator Smoking History Generator Microsimulation of individual smoking histories C++ code Inputs based on analysis of historical data on smoking patterns – 50 years of annual national cross-sectional surveys

Holford et al, AJPM 2014

Holford et al, AJPM 2014

Holford et al, AJPM 2014

CISNET public resources Historical US rates of initiation, cessation, cigarettes per day by gender, age and birth cohort Interactive graphs from publications https://resources.cisnet.cancer.gov/projects/

Simulating smoking related health outcomes Some groups use macro (population) models Others use microsimulations: Generate smoking histories for (large) N individuals for each birth-cohort from 1864-2050 using the SHG Use individual histories as inputs for lung cancer models Lung cancer incidence/mortality, overall mortality, impact of screening Large scale computations model calibrations / intervention simulation

High-Performance Computing Experiences Some groups run their models on their own PCs Others in Linux/Unix clusters Most run in single threads, but at least one or two groups have done some parallelization The Michigan group has compiled the SHG in the high-performance UM Flux cluster and is working on parallelizing the SHG and its tobacco control policies module Parallelization in a single processor multi-core workstation

McMahon et al, Plos One 2014

Sharing tools – publication support

Tobacco Policy Tool - Dissemination

Conclusions Examples of computational tools to support collaborative process Sharing inputs, models, outputs Modeling limited by computational power DOE collaboration Basis for new Center for the Assessment of the Public Health Impact of Tobacco Regulations proposal (U54)